A neural-symbolic system for automated assessment in training simulators - A position paper

Performance assessment in training simulators is a complex task. It requires monitoring and interpreting the student’s behaviour in the simulator using knowledge of the training task, the environment and a lot of experience. Assessment in simulators is therefore generally done by human observers. To capture this process in an automated system is challenging and requires innovative solutions. This paper proposes a new module for automated assessment in simulators that is based on Neural-Symbolic Learning and Reasoning and the Recurrent Temporal Restricted Boltzmann Machine (RTRBM). The module is capable of using existing and learning new rules for performance assessment, by observing experts and students performing the training tasks. These rules are used to validate and support the assessment process and to automatically assess student performance in a training simulator. The module will be developed in a three year research project on assessment in driving simulators for testing and examination.